كتاب تعليم العميق مع البايثون Deep Learning with Python
تحميل كتاب تعليم العميق مع البايثون Deep Learning with Python ، استكمالا لسلسلة تعلم نظم المعلومات الجغرافية Learn GIS نقدم لكم في هذه المقالة كتاب تعليم العميق مع البايثون Deep Learning with Python ، من تأليف FRANÇOIS CHOLLET.
Introduction book Deep Learning with Python
This e-book is an encyclopedia for Deep Learning with Python, as it contains 10 main topics, these topics are divided into many sub-topics, all in 700 pages, and these some topics are covered in the book Deep Learning with Python:-
Fundamentals of machine learning
After the three practical examples in chapter 3, you should be starting to feel familiar with how to approach classification and regression problems using neural networks, and you’ve witnessed the central problem of machine learning: overfitting. This chapter will formalize some of your new intuition into a solid conceptual framework for attacking and solving deep-learning problems. We’ll consolidate all of these concepts—model evaluation, data preprocessing and feature engineering, and tackling overfitting—into a detailed seven-step workflow for tackling any machine-learning task.
Four branches of machine learning
In our previous examples, you’ve become familiar with three specific types of machine-learning problems: binary classification, multiclass classification, and scalar regression. All three are instances of supervised learning, where the goal is to learn the relationship between training inputs and training targets.
Supervised learning is just the tip of the iceberg—machine learning is a vast field with a complex subfield taxonomy. Machine-learning algorithms generally fall into four broad categories, described in the following sections.
Unsupervised learning
This branch of machine learning consists of finding interesting transformations of the input data without the help of any targets, for the purposes of data visualization, data compression, or data denoising, or to better understand the correlations present in the data at hand. Unsupervised learning is the bread and butter of data analytics, and it’s often a necessary step in better understanding a dataset before attempting to solve a supervised-learning problem. Dimensionality reduction and clustering are well-known categories of unsupervised learning.
Self-supervised learning
This is a specific instance of supervised learning, but it’s different enough that it deserves its own category. Self-supervised learning is supervised learning without human-annotated labels—you can think of it as supervised learning without any humans in the loop. There are still labels involved (because the learning has to be supervised by something), but they’re generated from the input data, typically using a heuristic algorithm.
For instance, autoencoders are a well-known instance of self-supervised learning, where the generated targets are the input, unmodified. In the same way, trying to predict the next frame in a video, given past frames, or the next word in a text, given previous words, are instances of self-supervised learning (temporally supervised learning, in this case: supervision comes from future input data). Note that the distinction between supervised, self-supervised, and unsupervised learning can be blurry sometimes—these categories are more of a continuum without solid borders. Self-supervised learning can be reinterpreted as either supervised or unsupervised learning, depending on whether you pay attention to the learning mechanism or to the context of its application.
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